Plant Promoters and Terminators for High-Precision Bioengineering

Sourcing DNA parts for synthetic biology applications in plants

Transgenic approaches are now standard in plant biology research aiming to characterize gene function or improve crops. Recent advances in DNA synthesis and assembly make constructing transgenes a routine task. What remains nontrivial is the selection of the DNA parts and optimization of the transgene design. Early career researchers and seasoned molecular biologists alike often face difficult decisions on what promoter or terminator to use, what tag to include, and where to place it. This review aims to inform about the current approaches being employed to identify and characterize DNA parts with the desired functionalities and give general advice on basic construct design. Furthermore, we hope to share the excitement about new experimental and computational tools being developed in this field.

Deep learning the cis-regulatory code for gene expression in selected model plants

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Multifactorial analysis of terminator performance on heterologous gene expression in Physcomitrella

KEY MESSAGE

Characterization of Physcomitrella 3'UTRs across different promoters yields endogenous single and double terminators for usage in molecular pharming. The production of recombinant proteins for health applications accounts for a large share of the biopharmaceutical market. While many drugs are produced in microbial and mammalian systems, plants gain more attention as expression hosts to produce eukaryotic proteins. In particular, the good manufacturing practice (GMP)-compliant moss Physcomitrella (Physcomitrium patens) has outstanding features, such as excellent genetic amenability, reproducible bioreactor cultivation, and humanized protein glycosylation patterns. In this study, we selected and characterized novel terminators for their effects on heterologous gene expression. The Physcomitrella genome contains 53,346 unique 3'UTRs (untranslated regions) of which 7964 transcripts contain at least one intron. Over 91% of 3'UTRs exhibit more than one polyadenylation site, indicating the prevalence of alternative polyadenylation in Physcomitrella. Out of all 3'UTRs, 14 terminator candidates were selected and characterized via transient Dual-Luciferase assays, yielding a collection of endogenous terminators performing equally high as established heterologous terminators CaMV35S, AtHSP90, and NOS. High performing candidates were selected for testing as double terminators which impact reporter levels, dependent on terminator identity and positioning. Testing of 3'UTRs among the different promoters NOS, CaMV35S, and PpActin5 showed an increase of more than 1000-fold between promoters PpActin5 and NOS, whereas terminators increased reporter levels by less than tenfold, demonstrating the stronger effect promoters play as compared to terminators. Among selected terminator attributes, the number of polyadenylation sites as well as polyadenylation signals were found to influence terminator performance the most. Our results improve the biotechnology platform Physcomitrella and further our understanding of how terminators influence gene expression in plants in general.